Quantifying the Latent-Cause Inference Process in Humans Events in our lives can occur in seemingly random manner, yet we tend to make meaning by inferring underlying hidden, or latent, causes for events. When this process goes well, we accurately attribute our circumstances to their true cause such that we can behave appropriately and learn for the future. When this process goes wrong, we might make false attributions, overgeneralizing any negative outcomes to our own behavior, or inventing idiosyncratic accounts for every situation. We use latent cause inference in all aspects of our lives ? from interpreting our visual world to complex social decision-making. In my lab, we have developed computational models of latent-cause inference and used this framework to successfully predict learning (Gershman et al., 2013), memory (Gershman et al. 2014), and even social evaluation (Shin & Niv, under review). However, this previous work has focused on the conceptual level, only testing qualitative predictions of our framework. This is because there has been no task that allows the measurement and quantification of latent cause inference in individuals. Developing a precise, quantitative model of this process will be critical for understanding the neurobiological circuits that support successful inference as well as when and why they can fail. Computationally, the process of latent cause inference can be parameterized in a formal Bayesian model that relies on three parameters: how likely it is that a new cause occurs, how variable or homogeneous are the events that a cause tend to create, and how long is each cause active. Different people may have different settings for these parameters, corresponding to fundamental tendencies in interpreting the world that may vary across individuals and situations. Here, we develop a novel paradigm in which participants view ambiguous stimuli and cluster them according to their perceptual features. This allows us to quantify, for each individual, the parameters that they are using when making inferences. Thus, our task will allow precise quantification of the subprocesses involve in latent cause inference for the first time. In Aim 1, we will characterize latent-cause inference in a large online sample of human subjects and relate parameters of the inference process to transdiagnostic dimensional constructs of mental illness. In Aim 2, we will establish test-retest reliability of our measurements and determine whether parameters of the process correspond to stable individual traits, or rather follow the symptom state of the individual. This project will use state-of-the-art methods for characterizing complex cognitive processes using precise, quantitative models and collecting large-scale quantities of data by running experiments through an online platform. We will use the model to quantify the process of latent cause inference in individual subjects and map model parameters to self-report measures of mental-health related constructs. By using a variety of transdiagnostic questionnaires, we are well equipped to discover key factors that can be predicted by parameters of latent cause inference. Testing across a large, heterogeneous population (Aim 1) and across time points within subjects (Aim 2) will allow us to quantify the range of human latent- cause inference behavior and the reliability of these measures across situations (states) and individuals (traits). These findings will also inform our future fMRI studies using the same task to investigate the circuitry underlying this process.